Showing 2 results for Systemic Lupus Erythematosus.
Mahdieh Shojaa, Mehrdad Aghaie , Mahsa Amoli , Patricia Khashayar , Naemeh Javid, Fatemeh Shakeri, Mostafa Qorbani , Ramin Mohebbi,
Volume 73, Issue 2 (5-2015)
Abstract
Background: Cytotoxic lymphocyte antigen-4 (CTLA-4) plays an important role in regulating T cell activation. CTLA-4 gene polymorphisms are related with genetic susceptibility to various autoimmune diseases, including systemic lupus erythematosus (SLE). We analyzed the role of CTLA-4 polymorphisms at positions -318CT in patients who suffer from SLE.
Methods: This study was performed on 180 SLE patients referred to 5th Azar University Hospital in Gorgan, Iran. Three hundred and four ethnically-and age-matched healthy controls with no history of autoimmune diseases entered the study between 5th May 2008 and 23rd October 2009. DNA was extracted from blood samples according to the standard procedure. Polymerase chain reaction- restriction fragments length polymorphism (PCR-RFLP) was used to analyze the genotype and allele frequencies of this polymorphism. PCR was carried out using the following primers: forward 5′-AAATGAATTGGACTGGATGGT-3′ and reverse 5′-TTACGAGAAAGGAAGCCGT G-3′. The frequency of alleles and genotypes were assessed using direct counting. Chi-square test and Fisher’s exact test were used to compare the association between the alleles and genotype frequencies and SLE. P<0.05 were considered statistically significant.
Results: The CC genotype was observed in 94.5% of the SLE patients and 82.4% of the controls the difference was statistically significant (P=0.0001, OR=3.51, CI95%=1.77-7.53). The CT genotype, on the other hand, was more frequently observed in the control group (17.1% vs. 5.5%, P=0.0001, OR=0.28). T allele was significantly more common in the controls compared to SLE patients (P=0.0001, OR=0.26, CI95%=0.13-0.53).
Conclusion: Our results suggest that the -318C/T polymorphism of CTLA-4 gene might play a significant role in the genetic susceptibility to SLE. Therefore, further studies on populations, especially from other Middle East countries, are needed to confirm our results.
Mahmoud Akbarian , Khadijeh Paydar, Sharareh R Ostam Niakan Kalhori , Abbas Sheikhtaheri ,
Volume 73, Issue 4 (7-2015)
Abstract
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE.
Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix.
Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively.
Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables.
